Start by clarifying the board representation and rules (e.g., 'M' for mine, 'E' for empty, digits for adjacent mines). Then describe a BFS that starts from the clicked cell, reveals it, and if it's an empty cell with no adjacent mines, enqueues its neighbors. Use a queue and a visited set to avoid reprocessing cells.
Pro tip: Mention that you'll precompute adjacent mine counts only for revealed cells to save time, and use a 2D array or in-place modification to track visited states, avoiding extra space.
Ask about the input format (e.g., 2D char array), what characters represent mines, empty cells, and revealed cells. Confirm that clicking a mine ends the game and that revealing an empty cell with no adjacent mines should cascade.
Check if the click is out of bounds or on an already revealed cell. If the clicked cell is a mine, reveal it and return. Otherwise, proceed with BFS.
Initialize a queue with the clicked cell and a visited set. While the queue is not empty, dequeue a cell, reveal it, and if it's an empty cell with zero adjacent mines, enqueue all valid, unrevealed neighbors.
For each cell, count mines in its 8 neighboring cells. Only compute this when needed (i.e., when revealing a cell) to avoid unnecessary work.
After BFS completes, return the modified board. Ensure that all revealed cells are updated correctly and that unrevealed cells remain unchanged.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements (e.g., job granularity, execution guarantees, scale) and then outline a design using a priority queue for scheduling and a thread pool for execution. Emphasize thread-safety mechanisms like locks or concurrent data structures, and discuss trade-offs between precision, resource usage, and complexity.
Pro tip: Mention that you would use a single scheduler thread to avoid race conditions and simplify cancellation, and highlight how you'd handle long-running jobs without blocking the scheduler.
Ask about expected scale, job types (fixed-rate vs fixed-delay), execution guarantees (at-most-once, at-least-once), and whether jobs can be cancelled during execution.
Propose a scheduler with a priority queue (min-heap) for upcoming jobs and a thread pool for execution. Use a dedicated scheduler thread to manage the queue and dispatch jobs.
Explain how to protect shared state: use a lock (e.g., ReentrantLock) around the queue and job registry, or use concurrent data structures like ConcurrentHashMap and DelayQueue.
Detail register (add job to queue and registry), cancel (remove from queue and mark cancelled), start (launch scheduler thread), and stop (interrupt scheduler thread and shutdown pool gracefully).
Discuss trade-offs: precision vs. overhead, lock contention, handling missed executions, and graceful shutdown. Mention potential improvements like using a timing wheel for high-scale scenarios.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Structure your answer by addressing each concern (long-running tasks, cancellation, clock drift, backpressure) in turn, explaining the mechanisms you would use and the trade-offs involved. Emphasize how these mechanisms interact to ensure reliability and scalability, and relate them to real-world scenarios.
Pro tip: Demonstrate maturity by acknowledging that perfect clock synchronization is impossible and that backpressure is a feature, not a bug—show how you'd design for graceful degradation and observability.
Ask about task duration expectations, cancellation guarantees, clock synchronization assumptions, and throughput/latency requirements to tailor your solution.
Use asynchronous execution with heartbeats, checkpoints, and timeouts; consider dedicated worker pools or task queues to avoid blocking the scheduler.
Use cooperative cancellation via context propagation (e.g., context.Context in Go) and idempotent operations; ensure cleanup and resource release.
Use monotonic clocks for intervals, logical clocks or versioning for ordering, and NTP with drift compensation; avoid relying on wall-clock time for critical decisions.
Apply bounded queues, rate limiting, and load shedding; use feedback loops to adjust scheduling rate based on system load and downstream capacity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Per-job mutex or a running-state flag checked before dispatch.
Start by clarifying the requirements: is this a single-instance or distributed system, and what should happen if a job is already running (skip, queue, or fail)? Then present a layered solution, from simple in-process locks to distributed locks, and discuss trade-offs like lock expiration, failure handling, and idempotency.
Pro tip: Emphasize that preventing concurrent runs is only half the battle—you also need to handle stale locks and ensure jobs are idempotent, because distributed locks can fail. Mentioning this shows you think about real-world reliability, not just the happy path.
Ask whether the system is single-instance or distributed, and what the desired behavior is when a job is already running (skip, queue, or fail). This determines the complexity of the solution.
For single-instance, use in-process locks (e.g., mutex) or database-based locks. For distributed, use a distributed lock manager like Redis (Redlock), ZooKeeper, or a database with unique constraints.
Ensure locks are acquired atomically with a timeout and released reliably, even if the job crashes. Use unique tokens to avoid releasing someone else's lock.
Set lock expiration to avoid deadlocks, implement retries with backoff, and make jobs idempotent so repeated runs don't cause issues.
Track lock contention, job durations, and failures. Alert if locks are held too long or if jobs are skipped unexpectedly.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining what deterministic testing means for a scheduler: controlling time and concurrency to make tests repeatable. Then outline a strategy that combines injecting a fake clock, isolating scheduler logic from real-time dependencies, and using deterministic concurrency primitives. Finally, discuss trade-offs between test fidelity and simplicity, and how to verify correctness under various scenarios.
Pro tip: Emphasize that determinism is not just about time but also about controlling the order of task execution and randomness; mention using a single-threaded event loop or a deterministic executor in tests to avoid flakiness.
List all factors that make scheduler tests flaky: system clock, thread scheduling, random jitter, external dependencies, and asynchronous callbacks. Explain how each can be controlled.
Describe designing the scheduler with dependency injection for a clock and an executor. In tests, replace them with controllable implementations like a fake clock and a deterministic executor.
Outline specific test cases: tasks with different priorities, deadlines, periodic tasks, and edge cases like empty queues or simultaneous triggers. Use a step-by-step simulation to advance time and verify expected task order.
Explain how to assert that tasks execute in the correct order and at the correct virtual times. Use assertions on execution logs or callbacks, and check for no unexpected executions.
Acknowledge that deterministic tests may not cover real-world timing issues; suggest complementing with integration tests or property-based testing for broader coverage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: job types, frequency, reliability needs, and scale. Then propose a durable storage solution (e.g., database table) to persist job definitions and state, and describe how the scheduler loads and executes jobs on startup, ensuring idempotency and handling missed executions.
Pro tip: Emphasize the importance of idempotent job execution and discuss how to handle missed jobs during downtime, as this shows you understand real-world reliability concerns beyond just persistence.
Ask about job types (one-off vs recurring), required durability, expected scale, and tolerance for missed executions. This ensures your solution fits the context.
Select a durable store such as a relational database (e.g., PostgreSQL) or a distributed scheduler like Quartz with JDBC job store. Explain why it fits the requirements.
Define how job definitions, schedules, and execution state (e.g., last run, next run, status) are stored and updated. Include handling of concurrency and locking.
On process restart, load persisted jobs, compute next execution times, and handle missed jobs (e.g., execute immediately or skip based on policy). Ensure idempotency.
Discuss trade-offs like database load, latency, and complexity. Cover edge cases such as clock skew, job failures, and scaling across multiple instances.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.